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Record W4300529265 · doi:10.24124/2022/59297

An examination of conceptual knowledge using near-infrared spectroscopy and electroencephalography

2022· dissertation· en· W4300529265 on OpenAlexaff
Brian Duffels

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsUniversity of AlbertaUniversity of Northern British Columbia
Fundersnot available
KeywordsAmodal perceptionCognitive psychologyPsychologyTask (project management)CategorizationElectroencephalographySemantic memoryCognitionPerceptionGrounded theoryPrefrontal cortexBrain activity and meditationCognitive scienceComputer scienceArtificial intelligenceNeuroscienceQualitative research

Abstract

fetched live from OpenAlex

Traditionally, models of conceptual knowledge have relied upon amodal theories that largely overlook how environmental stimuli are converted into amodal representations and how perceptions reactivate these representations and translate them back into subjective modal experiences. Developed more recently, grounded cognition theories propose that physical experiences and conceptual knowledge rely, at least in part, on the same brain regions. Thus, conceptual knowledge is hypothesized to be experienced through the reactivation of the same brain regions that are activated during physical experiences with the environment. Furthermore, if these grounded hypotheses are correct, researchers should be able to observe predictable influences of grounded information on brain activity as well as participant response latencies and accuracy in experimental conditions. To this end, three experiments were conducted testing these hypotheses using semantic categorization tasks while simultaneous recordings were taken using functional near infrared spectroscopy (NIRS) and electroencephalography. It was hypothesized that the influence of automatically reactivated grounded information would be facilitatory (i.e., resulting in faster and more accurate responses for semantically richer words) when it was task congruent, but would be inhibitory (i.e., resulting in slower and less accurate responses for semantically richer words) when it was task incongruent, thus illustrating the automatic simulation of grounded information in the processing and retrieval of conceptual knowledge. NIRS was employed to monitor event-related patterns of prefrontal cortex (PFC) hemodynamics associated with these tasks. It was hypothesized that trials with high levels of task-relevant semantic information would be discernably different than those with low levels or those trials high in task-incongruent information. That is, given the high levels of task-relevant semantic information, these trials should be comparatively easier, thus requiring less activity in the PFC, resulting in less pronounced hemodynamic responses. Electroencephalography was employed to monitor the full-scalp event-related patterns of brain activity associated with the experimental tasks. It was hypothesized that event-related potential deflections and scalp topography would be able to discern qualitatively and quantitatively different patterns of activity as a function of the amount and relevance of grounded information obtained through physical and emotional experiences with the word stimuli’s referents. The behavioural, accuracy, and electroencephalography data generally support these hypotheses. When a stimulus’s grounded information is high and task relevant, participants responded more quickly and accurately, and had discernably different patterns of brain activity than when a stimulus’s grounded information was low and task relevant. When a stimulus’s grounded information was high and task-irrelevant, participants were slower and less accurate, and exhibited patterns of brain activity that reflected both the additional semantic information and the additional processing necessary to reconcile the task incongruence. Unfortunately, data obtained from NIRS failed to illustrate meaningful condition differences. Possible reasons for this are discussed in detail in Chapter 3. Collectively, the data presented in this dissertation serve to advance and extend the claims made by grounded cognition theorists by illustrating the automatic simulation of information obtained through interactions with the environment. Further research is required to extend this work to other brain regions and to develop NIRS methods that can address these research questions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.317
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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